Attending to Characters in Neural Sequence Labeling Models (1611.04361v1)

Abstract: Sequence labeling architectures use word embeddings for capturing similarity, but suffer when handling previously unseen or rare words. We investigate character-level extensions to such models and propose a novel architecture for combining alternative word representations. By using an attention mechanism, the model is able to dynamically decide how much information to use from a word- or character-level component. We evaluated different architectures on a range of sequence labeling datasets, and character-level extensions were found to improve performance on every benchmark. In addition, the proposed attention-based architecture delivered the best results even with a smaller number of trainable parameters.

Analyzing Character-Level Contributions in Neural Sequence Labeling Models

"Attending to Characters in Neural Sequence Labeling Models" presents an innovative exploration of incorporating character-level features into neural sequence labeling architectures. The paper, led by Marek Rei and collaborators, investigates the limitations of traditional word embedding approaches, particularly in handling rare or out-of-vocabulary (OOV) words, and proposes a strategy to address these weaknesses through character-based extensions.

The authors identify sequence labeling as a crucial component of various NLP tasks, including named entity recognition (NER), part-of-speech (POS) tagging, and shallow parsing. Traditional sequence labeling systems have relied heavily on task-specific feature engineering, whereas recent advancements have enabled the automatic extraction of useful features from data, primarily harnessing word embeddings. These embeddings work effectively for semantically and functionally similar words but falter when confronted with OOV or infrequently encountered tokens, highlighting a critical area for improvement.

The primary contribution of this paper is the introduction of a novel architecture that employs an attention mechanism to merge word- and character-level representations dynamically. By leveraging bi-directional Long Short-Term Memory (LSTM) models at both word and character levels, the proposed system can discern valuable features that enhance model performance across different datasets. The attention-based mechanism provides the model with the flexibility to weigh the contributions of word and character inputs, a significant improvement over simple concatenation methods used in previous studies.

Empirical evaluation across eight distinct datasets spanning domains such as biomedical NER and error detection demonstrates consistent performance improvements with the integration of character-level components. All benchmarks reported in the paper indicate that character-based features enhance the handling of morphological variations and rare word occurrences. Notably, the proposed attention-based architecture achieved superior results on all datasets, demonstrating its ability to efficiently manage the trade-off between model complexity and performance.

The practical implications of these findings are substantial for NLP, where the ability to generalize from character-level data significantly enhances the model's versatility and robustness, particularly in scenarios with limited exposure to specific vocabulary items. Theoretical advancements are evident in the incorporation of a dual-objective training approach, where character-level representations are optimized to align with high-quality word embeddings, enriching the shared feature space.

Future explorations could focus on refining these character-level mechanisms further, examining their applicability to other NLP tasks beyond sequence labeling. Employing such finely tuned character representations could benefit text generation, sentiment analysis, and more complex linguistic modeling. Additionally, investigations into the computational efficiency and scalability of these approaches would be beneficial for their deployment in real-world NLP applications.

Ultimately, the paper sets a pivotal precedent for enhancing existing neural architectures with sub-word level analysis, underscoring the significance of attending to character-level details in facilitating robust and flexible NLP applications.